3 of the Most Down-to-Earth Ways We Used Big Data in 2016

Predictive business analytics isn’t science fiction. Right now there are enterprises in consumer and business markets that are using big data to match inventory counts to customer demand in real-time. In fact, the best and brightest of these enterprises are able to use leading indicators and current market activity, in order to anticipate a potential increase or decrease in customer demand and respond immediately.

Companies no longer have to spend an inordinate amount of time reviewing past sales figures and inventory turnover rates in order to have a better handle on future demand. In fact, if you’re spending too much time analyzing past performance and reviewing historical sales figures, then you’re missing out on the opportunities right in front of you. Here are some of the most down-to-earth ways companies are using big data in 2016.

Retailers Leveraging Big Data

Retailers are leveraging big data in real-time so that they’re able to match their pricing strategy, inventory levels and special discounted offers to what customers want right now. The impetus is to be proactive and ensure that sales are driven by real-time market feedback, instead of being reactive, and having to ask weeks or months later why sales targets fell short.

A perfect example includes Walmart’s Data Café. The members of this tech-savvy team focus on better understanding what’s happening at this very moment and what must be done right now to alter course, if needed. Members of the Data Café can quickly disseminate why customer demand for a given product has suddenly halted, or why certain locations have an overstock situation. Are low sales caused by a pricing error? Were products not properly promoted? Was an in-transit shipment improperly re-routed?

Mobile handheld computers and in-transit radio frequency identification (RFID) tracking can only get you so far. In the end, you need the team to analyze the results and react accordingly. Walmart’s Data Café team is able to do that right now. What used to take weeks to rectify now takes mere minutes.

Manufacturers and Big Data

Manufacturers are using big data analytics to upgrade the quality of their product, improve demand forecasting, maximize machine utilization, and properly schedule maintenance and upkeep. They can quickly ascertain the impact of out-of-spec raw material on downstream operations, or assess the damages caused by late incoming shipments from vendors.

Manufactures can immediately determine the impact on machine utilization when faced with an unexpected increase in market demand, or determine the impact of downtime from one portion of their production process to the next. They can use seasonality, and the fluctuating business cycles within their market, in order to properly schedule maintenance and keep downtime to a minimum. The more data available to manufacturers, the stronger their proactive response.

Data Analytics and the Healthcare Industry

If ever there was a match made in heaven, this would surely be it. There is no better payoff for big data analytics than to see its role in saving lives. However, when it comes to the healthcare industry, it wasn’t a problem of having data. Instead, it was a problem of structuring that data. Nowadays, doctors are able to improve diagnoses, shorten wait times, offer more applicable prescriptions and use big data analytics to better anticipate future patient issues. Big data solutions are being used to produce predictive models that use electronic data recording systems that help to improve wait times and upgrade out-patient services.

Big data is here to stay and it’s being leveraged at this very moment by all kinds of forward-looking enterprises and institutions. These parties have decided to take charge of the here and now by ensuring that costs are minimized, wait times are reduced, and every customer is properly serviced in real-time.

If you have a question about how big data can impact your business, please contact us now for more information.

Steven Linwood is a contributing editor at Data Insider and writes about all things analytics, technology and statistics.